论文标题
使用编码器 - 模特神经网络对地震相的二元分割
Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks
论文作者
论文摘要
地震数据的解释对于在地质研究领域表征沉积物的形状至关重要。在地震解释中，深度学习对于减少对手工相分段的几何形状的依赖和研究地质区域所需的时间很有用。这项工作提出了一个深层神经网络，用于相分段（DNF），以获得地震相分段的最新结果。 DNFS是使用跨透镜和JACCARD损失函数的组合训练的。我们的结果表明，DNF使用比STNET和U-NET更少的参数对地震相分割的高度详细预测。
The interpretation of seismic data is vital for characterizing sediments' shape in areas of geological study. In seismic interpretation, deep learning becomes useful for reducing the dependence on handcrafted facies segmentation geometry and the time required to study geological areas. This work presents a Deep Neural Network for Facies Segmentation (DNFS) to obtain state-of-the-art results for seismic facies segmentation. DNFS is trained using a combination of cross-entropy and Jaccard loss functions. Our results show that DNFS obtains highly detailed predictions for seismic facies segmentation using fewer parameters than StNet and U-Net.
